Overview

Dataset statistics

Number of variables23
Number of observations3677
Missing cells9171
Missing cells (%)10.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory631.8 B

Variable types

Text3
Categorical10
Numeric10

Alerts

store room is highly imbalanced (55.7%)Imbalance
floorNum has 2431 (66.1%) missing valuesMissing
facing has 1045 (28.4%) missing valuesMissing
super_built_up_area has 1802 (49.0%) missing valuesMissing
built_up_area has 2036 (55.4%) missing valuesMissing
carpet_area has 1805 (49.1%) missing valuesMissing
area is highly skewed (γ1 = 29.73095338)Skewed
built_up_area is highly skewed (γ1 = 40.14464398)Skewed
carpet_area is highly skewed (γ1 = 24.33323909)Skewed
luxury_score has 481 (13.1%) zerosZeros

Reproduction

Analysis started2024-02-13 04:16:15.312464
Analysis finished2024-02-13 04:16:24.723817
Duration9.41 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Distinct675
Distinct (%)18.4%
Missing1
Missing (%)< 0.1%
Memory size293.9 KiB
2024-02-12T22:16:24.889304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.863166
Min length1

Characters and Unicode

Total characters61989
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique307 ?
Unique (%)8.4%

Sample

1st rowthe lions cghs
2nd rowbestech park view residency
3rd rowbptp freedom park life
4th rowss the leaf
5th rowvatika city homes
ValueCountFrequency (%)
independent 491
 
5.1%
the 350
 
3.6%
dlf 220
 
2.3%
park 209
 
2.2%
city 166
 
1.7%
emaar 155
 
1.6%
global 153
 
1.6%
m3m 152
 
1.6%
signature 150
 
1.6%
heights 134
 
1.4%
Other values (781) 7497
77.5%
2024-02-12T22:16:25.231538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6701
 
10.8%
6003
 
9.7%
a 5864
 
9.5%
r 4171
 
6.7%
n 4160
 
6.7%
i 3827
 
6.2%
t 3716
 
6.0%
s 3472
 
5.6%
l 2943
 
4.7%
o 2755
 
4.4%
Other values (31) 18377
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55441
89.4%
Space Separator 6003
 
9.7%
Decimal Number 527
 
0.9%
Other Punctuation 10
 
< 0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6701
12.1%
a 5864
 
10.6%
r 4171
 
7.5%
n 4160
 
7.5%
i 3827
 
6.9%
t 3716
 
6.7%
s 3472
 
6.3%
l 2943
 
5.3%
o 2755
 
5.0%
d 2482
 
4.5%
Other values (16) 15350
27.7%
Decimal Number
ValueCountFrequency (%)
3 207
39.3%
2 82
 
15.6%
1 75
 
14.2%
6 56
 
10.6%
8 32
 
6.1%
4 19
 
3.6%
5 17
 
3.2%
0 13
 
2.5%
9 13
 
2.5%
7 13
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 7
70.0%
/ 2
 
20.0%
. 1
 
10.0%
Space Separator
ValueCountFrequency (%)
6003
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55441
89.4%
Common 6548
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6701
12.1%
a 5864
 
10.6%
r 4171
 
7.5%
n 4160
 
7.5%
i 3827
 
6.9%
t 3716
 
6.7%
s 3472
 
6.3%
l 2943
 
5.3%
o 2755
 
5.0%
d 2482
 
4.5%
Other values (16) 15350
27.7%
Common
ValueCountFrequency (%)
6003
91.7%
3 207
 
3.2%
2 82
 
1.3%
1 75
 
1.1%
6 56
 
0.9%
8 32
 
0.5%
4 19
 
0.3%
5 17
 
0.3%
0 13
 
0.2%
9 13
 
0.2%
Other values (5) 31
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61989
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6701
 
10.8%
6003
 
9.7%
a 5864
 
9.5%
r 4171
 
6.7%
n 4160
 
6.7%
i 3827
 
6.2%
t 3716
 
6.0%
s 3472
 
5.6%
l 2943
 
4.7%
o 2755
 
4.4%
Other values (31) 18377
29.6%

property_type
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size248.6 KiB
flat
2818 
house
859 

Length

Max length5
Median length4
Mean length4.2336144
Min length4

Characters and Unicode

Total characters15567
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2818
76.6%
house 859
 
23.4%

Length

2024-02-12T22:16:25.353464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T22:16:25.433564image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
flat 2818
76.6%
house 859
 
23.4%

Most occurring characters

ValueCountFrequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15567
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 15567
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15567
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

sector
Text

Distinct113
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size266.9 KiB
2024-02-12T22:16:25.566531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length26
Median length9
Mean length9.3209138
Min length7

Characters and Unicode

Total characters34273
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 56
2nd rowsector 2
3rd rowsector 57
4th rowsector 85
5th rowsector 83
ValueCountFrequency (%)
sector 3452
46.8%
road 178
 
2.4%
sohna 166
 
2.2%
85 108
 
1.5%
102 107
 
1.4%
92 100
 
1.4%
69 93
 
1.3%
90 89
 
1.2%
81 87
 
1.2%
65 87
 
1.2%
Other values (106) 2915
39.5%
2024-02-12T22:16:25.835088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3807
11.1%
3705
10.8%
s 3697
10.8%
r 3697
10.8%
e 3542
10.3%
c 3503
10.2%
t 3463
10.1%
1 1076
 
3.1%
0 804
 
2.3%
8 780
 
2.3%
Other values (21) 6199
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23299
68.0%
Decimal Number 7269
 
21.2%
Space Separator 3705
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3807
16.3%
s 3697
15.9%
r 3697
15.9%
e 3542
15.2%
c 3503
15.0%
t 3463
14.9%
a 699
 
3.0%
d 249
 
1.1%
n 221
 
0.9%
h 203
 
0.9%
Other values (10) 218
 
0.9%
Decimal Number
ValueCountFrequency (%)
1 1076
14.8%
0 804
11.1%
8 780
10.7%
9 764
10.5%
6 742
10.2%
7 684
9.4%
2 676
9.3%
3 666
9.2%
5 593
8.2%
4 484
6.7%
Space Separator
ValueCountFrequency (%)
3705
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23299
68.0%
Common 10974
32.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3807
16.3%
s 3697
15.9%
r 3697
15.9%
e 3542
15.2%
c 3503
15.0%
t 3463
14.9%
a 699
 
3.0%
d 249
 
1.1%
n 221
 
0.9%
h 203
 
0.9%
Other values (10) 218
 
0.9%
Common
ValueCountFrequency (%)
3705
33.8%
1 1076
 
9.8%
0 804
 
7.3%
8 780
 
7.1%
9 764
 
7.0%
6 742
 
6.8%
7 684
 
6.2%
2 676
 
6.2%
3 666
 
6.1%
5 593
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34273
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 3807
11.1%
3705
10.8%
s 3697
10.8%
r 3697
10.8%
e 3542
10.3%
c 3503
10.2%
t 3463
10.1%
1 1076
 
3.1%
0 804
 
2.3%
8 780
 
2.3%
Other values (21) 6199
18.1%

price
Real number (ℝ)

Distinct473
Distinct (%)12.9%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.5336639
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-02-12T22:16:25.962665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.52
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.9806235
Coefficient of variation (CV)1.1764084
Kurtosis14.933373
Mean2.5336639
Median Absolute Deviation (MAD)0.72
Skewness3.2791705
Sum9273.21
Variance8.8841164
MonotonicityNot monotonic
2024-02-12T22:16:26.064932image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 80
 
2.2%
1.2 64
 
1.7%
1.5 64
 
1.7%
0.9 63
 
1.7%
1.1 62
 
1.7%
1.4 60
 
1.6%
1.3 57
 
1.6%
2 52
 
1.4%
0.95 52
 
1.4%
1.6 48
 
1.3%
Other values (463) 3058
83.2%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 8
0.2%
0.21 6
0.2%
0.22 8
0.2%
0.23 1
 
< 0.1%
0.24 6
0.2%
0.25 11
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

Distinct2651
Distinct (%)72.4%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13892.668
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-02-12T22:16:26.167430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4715.95
Q16817.25
median9020
Q313880.5
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7063.25

Descriptive statistics

Standard deviation23210.067
Coefficient of variation (CV)1.6706702
Kurtosis186.92801
Mean13892.668
Median Absolute Deviation (MAD)2794
Skewness11.43719
Sum50847166
Variance5.3870722 × 108
MonotonicityNot monotonic
2024-02-12T22:16:26.271781image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27
 
0.7%
8000 19
 
0.5%
5000 17
 
0.5%
12500 14
 
0.4%
11111 13
 
0.4%
6666 13
 
0.4%
22222 13
 
0.4%
7500 12
 
0.3%
8333 12
 
0.3%
6000 11
 
0.3%
Other values (2641) 3509
95.4%
(Missing) 17
 
0.5%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

SKEWED 

Distinct2762
Distinct (%)75.5%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2888.3887
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-02-12T22:16:26.376606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile518.8855
Q11231.97
median1733.015
Q32300.095
95-th percentile4245.8975
Maximum875000
Range874950
Interquartile range (IQR)1068.125

Descriptive statistics

Standard deviation23167.504
Coefficient of variation (CV)8.0209095
Kurtosis942.02894
Mean2888.3887
Median Absolute Deviation (MAD)532.945
Skewness29.730953
Sum10571503
Variance5.3673325 × 108
MonotonicityNot monotonic
2024-02-12T22:16:26.478752image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 33
 
0.9%
1000 18
 
0.5%
1650.17 17
 
0.5%
1250 13
 
0.4%
500 11
 
0.3%
900.09 10
 
0.3%
1650.05 10
 
0.3%
900 9
 
0.2%
1350.01 9
 
0.2%
1800.01 9
 
0.2%
Other values (2752) 3521
95.8%
(Missing) 17
 
0.5%
ValueCountFrequency (%)
50 4
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
0.1%
61 1
 
< 0.1%
67 2
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
76 1
 
< 0.1%
ValueCountFrequency (%)
875000 1
< 0.1%
642857.14 1
< 0.1%
620000 1
< 0.1%
566666.67 1
< 0.1%
215517.24 1
< 0.1%
98977.95 1
< 0.1%
82781.46 1
< 0.1%
65517.24 2
0.1%
65261.04 1
< 0.1%
58227.85 1
< 0.1%
Distinct2355
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size428.2 KiB
2024-02-12T22:16:26.679987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length124
Median length119
Mean length54.236062
Min length12

Characters and Unicode

Total characters199426
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1849 ?
Unique (%)50.3%

Sample

1st rowSuper Built up area 2400(222.97 sq.m.)Built Up area: 2000 sq.ft. (185.81 sq.m.)Carpet area: 1800 sq.ft. (167.23 sq.m.)
2nd rowSuper Built up area 1565(145.39 sq.m.)
3rd rowBuilt Up area: 5010 (465.44 sq.m.)
4th rowSuper Built up area 1741(161.74 sq.m.)Built Up area: 1730 sq.ft. (160.72 sq.m.)Carpet area: 1720 sq.ft. (159.79 sq.m.)
5th rowSuper Built up area 1740(161.65 sq.m.)Carpet area: 1225 sq.ft. (113.81 sq.m.)
ValueCountFrequency (%)
area 5573
18.5%
sq.m 3655
12.1%
up 3020
 
10.0%
built 2316
 
7.7%
super 1875
 
6.2%
sq.ft 1751
 
5.8%
sq.m.)carpet 1185
 
3.9%
sq.m.)built 702
 
2.3%
carpet 683
 
2.3%
plot 681
 
2.3%
Other values (2846) 8700
28.9%
2024-02-12T22:16:27.020464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26464
 
13.3%
. 20389
 
10.2%
a 13154
 
6.6%
r 9456
 
4.7%
e 9320
 
4.7%
1 9205
 
4.6%
s 7567
 
3.8%
q 7431
 
3.7%
t 7324
 
3.7%
u 6770
 
3.4%
Other values (25) 82346
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82758
41.5%
Decimal Number 47135
23.6%
Space Separator 26464
 
13.3%
Other Punctuation 23406
 
11.7%
Uppercase Letter 8593
 
4.3%
Close Punctuation 5535
 
2.8%
Open Punctuation 5535
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13154
15.9%
r 9456
11.4%
e 9320
11.3%
s 7567
9.1%
q 7431
9.0%
t 7324
8.8%
u 6770
8.2%
p 6767
8.2%
m 5544
6.7%
l 3701
 
4.5%
Other values (5) 5724
6.9%
Decimal Number
ValueCountFrequency (%)
1 9205
19.5%
0 6628
14.1%
2 5688
12.1%
5 4714
10.0%
3 3960
8.4%
4 3711
7.9%
6 3674
 
7.8%
7 3254
 
6.9%
8 3157
 
6.7%
9 3144
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 3020
35.1%
S 1875
21.8%
C 1872
21.8%
U 1145
 
13.3%
P 681
 
7.9%
Other Punctuation
ValueCountFrequency (%)
. 20389
87.1%
: 3017
 
12.9%
Space Separator
ValueCountFrequency (%)
26464
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5535
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5535
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108075
54.2%
Latin 91351
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13154
14.4%
r 9456
10.4%
e 9320
10.2%
s 7567
8.3%
q 7431
8.1%
t 7324
8.0%
u 6770
7.4%
p 6767
7.4%
m 5544
 
6.1%
l 3701
 
4.1%
Other values (10) 14317
15.7%
Common
ValueCountFrequency (%)
26464
24.5%
. 20389
18.9%
1 9205
 
8.5%
0 6628
 
6.1%
2 5688
 
5.3%
) 5535
 
5.1%
( 5535
 
5.1%
5 4714
 
4.4%
3 3960
 
3.7%
4 3711
 
3.4%
Other values (5) 16246
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 199426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26464
 
13.3%
. 20389
 
10.2%
a 13154
 
6.6%
r 9456
 
4.7%
e 9320
 
4.7%
1 9205
 
4.6%
s 7567
 
3.8%
q 7431
 
3.7%
t 7324
 
3.7%
u 6770
 
3.4%
Other values (25) 82346
41.3%

bedRoom
Real number (ℝ)

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3600761
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-02-12T22:16:27.234650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8976289
Coefficient of variation (CV)0.56475771
Kurtosis18.212873
Mean3.3600761
Median Absolute Deviation (MAD)1
Skewness3.4851418
Sum12355
Variance3.6009954
MonotonicityNot monotonic
2024-02-12T22:16:27.326633image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1496
40.7%
2 942
25.6%
4 660
17.9%
5 210
 
5.7%
1 124
 
3.4%
6 74
 
2.0%
9 41
 
1.1%
8 30
 
0.8%
12 28
 
0.8%
7 28
 
0.8%
Other values (9) 44
 
1.2%
ValueCountFrequency (%)
1 124
 
3.4%
2 942
25.6%
3 1496
40.7%
4 660
17.9%
5 210
 
5.7%
6 74
 
2.0%
7 28
 
0.8%
8 30
 
0.8%
9 41
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.8%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4245309
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-02-12T22:16:27.415151image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9480681
Coefficient of variation (CV)0.56885693
Kurtosis17.542297
Mean3.4245309
Median Absolute Deviation (MAD)1
Skewness3.2488298
Sum12592
Variance3.7949693
MonotonicityNot monotonic
2024-02-12T22:16:27.505474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1077
29.3%
2 1047
28.5%
4 820
22.3%
5 294
 
8.0%
1 156
 
4.2%
6 117
 
3.2%
9 41
 
1.1%
7 40
 
1.1%
8 25
 
0.7%
12 22
 
0.6%
Other values (9) 38
 
1.0%
ValueCountFrequency (%)
1 156
 
4.2%
2 1047
28.5%
3 1077
29.3%
4 820
22.3%
5 294
 
8.0%
6 117
 
3.2%
7 40
 
1.1%
8 25
 
0.7%
9 41
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size238.2 KiB
3+
1172 
3
1074 
2
884 
1
365 
0
 
96

Length

Max length2
Median length1
Mean length1.3421267
Min length1

Characters and Unicode

Total characters4935
Distinct characters7
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3+
2nd row3
3rd row3+
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3+ 1172
31.9%
3 1074
29.2%
2 884
24.0%
1 365
 
9.9%
0 96
 
2.6%
No 86
 
2.3%

Length

2024-02-12T22:16:27.599238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T22:16:27.682257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2246
61.1%
2 884
 
24.0%
1 365
 
9.9%
0 96
 
2.6%
no 86
 
2.3%

Most occurring characters

ValueCountFrequency (%)
3 2246
45.5%
+ 1172
23.7%
2 884
 
17.9%
1 365
 
7.4%
0 96
 
1.9%
N 86
 
1.7%
o 86
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3591
72.8%
Math Symbol 1172
 
23.7%
Uppercase Letter 86
 
1.7%
Lowercase Letter 86
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2246
62.5%
2 884
 
24.6%
1 365
 
10.2%
0 96
 
2.7%
Math Symbol
ValueCountFrequency (%)
+ 1172
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 86
100.0%
Lowercase Letter
ValueCountFrequency (%)
o 86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4763
96.5%
Latin 172
 
3.5%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2246
47.2%
+ 1172
24.6%
2 884
 
18.6%
1 365
 
7.7%
0 96
 
2.0%
Latin
ValueCountFrequency (%)
N 86
50.0%
o 86
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4935
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2246
45.5%
+ 1172
23.7%
2 884
 
17.9%
1 365
 
7.4%
0 96
 
1.9%
N 86
 
1.7%
o 86
 
1.7%

floorNum
Real number (ℝ)

MISSING 

Distinct20
Distinct (%)1.6%
Missing2431
Missing (%)66.1%
Infinite0
Infinite (%)0.0%
Mean4.0401284
Minimum0
Maximum51
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-02-12T22:16:27.771553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q34
95-th percentile12
Maximum51
Range51
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.4260994
Coefficient of variation (CV)1.0955343
Kurtosis16.524098
Mean4.0401284
Median Absolute Deviation (MAD)1
Skewness3.2024875
Sum5034
Variance19.590356
MonotonicityNot monotonic
2024-02-12T22:16:27.857098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 493
 
13.4%
3 273
 
7.4%
12 158
 
4.3%
1 152
 
4.1%
4 127
 
3.5%
22 13
 
0.4%
5 8
 
0.2%
14 3
 
0.1%
0 3
 
0.1%
6 3
 
0.1%
Other values (10) 13
 
0.4%
(Missing) 2431
66.1%
ValueCountFrequency (%)
0 3
 
0.1%
1 152
 
4.1%
2 493
13.4%
3 273
7.4%
4 127
 
3.5%
5 8
 
0.2%
6 3
 
0.1%
10 2
 
0.1%
11 2
 
0.1%
12 158
 
4.3%
ValueCountFrequency (%)
51 1
 
< 0.1%
33 1
 
< 0.1%
32 2
 
0.1%
27 1
 
< 0.1%
22 13
0.4%
21 1
 
< 0.1%
20 1
 
< 0.1%
16 1
 
< 0.1%
14 3
 
0.1%
13 1
 
< 0.1%

facing
Categorical

MISSING 

Distinct8
Distinct (%)0.3%
Missing1045
Missing (%)28.4%
Memory size250.0 KiB
East
623 
North-East
623 
North
387 
West
249 
South
231 
Other values (3)
519 

Length

Max length10
Median length5
Mean length6.8381459
Min length4

Characters and Unicode

Total characters17998
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEast
2nd rowSouth-West
3rd rowEast
4th rowSouth-East
5th rowSouth-East

Common Values

ValueCountFrequency (%)
East 623
16.9%
North-East 623
16.9%
North 387
 
10.5%
West 249
 
6.8%
South 231
 
6.3%
North-West 193
 
5.2%
South-East 173
 
4.7%
South-West 153
 
4.2%
(Missing) 1045
28.4%

Length

2024-02-12T22:16:27.951342image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T22:16:28.039565image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
east 623
23.7%
north-east 623
23.7%
north 387
14.7%
west 249
 
9.5%
south 231
 
8.8%
north-west 193
 
7.3%
south-east 173
 
6.6%
south-west 153
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t 3774
21.0%
s 2014
11.2%
o 1760
9.8%
h 1760
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13082
72.7%
Uppercase Letter 3774
 
21.0%
Dash Punctuation 1142
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3774
28.8%
s 2014
15.4%
o 1760
13.5%
h 1760
13.5%
a 1419
 
10.8%
r 1203
 
9.2%
e 595
 
4.5%
u 557
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
E 1419
37.6%
N 1203
31.9%
W 595
15.8%
S 557
 
14.8%
Dash Punctuation
ValueCountFrequency (%)
- 1142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16856
93.7%
Common 1142
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3774
22.4%
s 2014
11.9%
o 1760
10.4%
h 1760
10.4%
E 1419
 
8.4%
a 1419
 
8.4%
N 1203
 
7.1%
r 1203
 
7.1%
W 595
 
3.5%
e 595
 
3.5%
Other values (2) 1114
 
6.6%
Common
ValueCountFrequency (%)
- 1142
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3774
21.0%
s 2014
11.2%
o 1760
9.8%
h 1760
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

agePossession
Categorical

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size280.3 KiB
relatively new
1646 
new property
593 
moderately new
563 
Undefined
437 
old property
303 
Other values (2)
 
135

Length

Max length18
Median length14
Mean length13.062823
Min length9

Characters and Unicode

Total characters48032
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowold property
2nd rowmoderately new
3rd rowmoderately new
4th rowrelatively new
5th rowmoderately new

Common Values

ValueCountFrequency (%)
relatively new 1646
44.8%
new property 593
 
16.1%
moderately new 563
 
15.3%
Undefined 437
 
11.9%
old property 303
 
8.2%
under construction 134
 
3.6%
undefined 1
 
< 0.1%

Length

2024-02-12T22:16:28.141560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T22:16:28.224878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
new 2802
40.5%
relatively 1646
23.8%
property 896
 
13.0%
moderately 563
 
8.1%
undefined 438
 
6.3%
old 303
 
4.4%
under 134
 
1.9%
construction 134
 
1.9%

Most occurring characters

ValueCountFrequency (%)
e 9126
19.0%
r 4269
8.9%
l 4158
8.7%
n 4080
8.5%
t 3373
 
7.0%
3239
 
6.7%
y 3105
 
6.5%
w 2802
 
5.8%
i 2218
 
4.6%
a 2209
 
4.6%
Other values (10) 9453
19.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 44356
92.3%
Space Separator 3239
 
6.7%
Uppercase Letter 437
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9126
20.6%
r 4269
9.6%
l 4158
9.4%
n 4080
9.2%
t 3373
 
7.6%
y 3105
 
7.0%
w 2802
 
6.3%
i 2218
 
5.0%
a 2209
 
5.0%
o 2030
 
4.6%
Other values (8) 6986
15.7%
Space Separator
ValueCountFrequency (%)
3239
100.0%
Uppercase Letter
ValueCountFrequency (%)
U 437
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44793
93.3%
Common 3239
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9126
20.4%
r 4269
9.5%
l 4158
9.3%
n 4080
9.1%
t 3373
 
7.5%
y 3105
 
6.9%
w 2802
 
6.3%
i 2218
 
5.0%
a 2209
 
4.9%
o 2030
 
4.5%
Other values (9) 7423
16.6%
Common
ValueCountFrequency (%)
3239
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9126
19.0%
r 4269
8.9%
l 4158
8.7%
n 4080
8.5%
t 3373
 
7.0%
3239
 
6.7%
y 3105
 
6.5%
w 2802
 
5.8%
i 2218
 
4.6%
a 2209
 
4.6%
Other values (10) 9453
19.7%

super_built_up_area
Real number (ℝ)

MISSING 

Distinct593
Distinct (%)31.6%
Missing1802
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean1925.2376
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-02-12T22:16:28.337632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile767
Q11479.5
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)735.5

Descriptive statistics

Standard deviation764.17218
Coefficient of variation (CV)0.39692356
Kurtosis10.349191
Mean1925.2376
Median Absolute Deviation (MAD)372
Skewness1.8364563
Sum3609820.5
Variance583959.12
MonotonicityNot monotonic
2024-02-12T22:16:28.446036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 37
 
1.0%
1950 37
 
1.0%
2000 25
 
0.7%
1578 25
 
0.7%
1640 22
 
0.6%
2150 22
 
0.6%
1900 19
 
0.5%
2408 19
 
0.5%
1930 18
 
0.5%
2812 17
 
0.5%
Other values (583) 1634
44.4%
(Missing) 1802
49.0%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

MISSING  SKEWED 

Distinct626
Distinct (%)38.1%
Missing2036
Missing (%)55.4%
Infinite0
Infinite (%)0.0%
Mean2394.3309
Minimum30
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-02-12T22:16:28.546796image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile267
Q11125
median1650
Q32400
95-th percentile4680
Maximum737147
Range737117
Interquartile range (IQR)1275

Descriptive statistics

Standard deviation18203.807
Coefficient of variation (CV)7.6028787
Kurtosis1621.2744
Mean2394.3309
Median Absolute Deviation (MAD)600
Skewness40.144644
Sum3929097
Variance3.3137861 × 108
MonotonicityNot monotonic
2024-02-12T22:16:28.653772image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 41
 
1.1%
3240 36
 
1.0%
1900 34
 
0.9%
2700 33
 
0.9%
1350 33
 
0.9%
900 27
 
0.7%
1600 26
 
0.7%
1300 24
 
0.7%
2000 24
 
0.7%
1700 23
 
0.6%
Other values (616) 1340
36.4%
(Missing) 2036
55.4%
ValueCountFrequency (%)
30 1
 
< 0.1%
50 3
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 4
0.1%
61 1
 
< 0.1%
62 1
 
< 0.1%
67 2
0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
 
0.1%
7450 1
 
< 0.1%
7331 2
 
0.1%

carpet_area
Real number (ℝ)

MISSING  SKEWED 

Distinct733
Distinct (%)39.2%
Missing1805
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean2529.1795
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-02-12T22:16:28.760086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1843
median1300
Q31790
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)947

Descriptive statistics

Standard deviation22799.836
Coefficient of variation (CV)9.0147166
Kurtosis604.53764
Mean2529.1795
Median Absolute Deviation (MAD)472.5
Skewness24.333239
Sum4734624
Variance5.1983254 × 108
MonotonicityNot monotonic
2024-02-12T22:16:28.865542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1800 35
 
1.0%
1600 35
 
1.0%
1200 31
 
0.8%
1500 29
 
0.8%
1650 28
 
0.8%
1350 27
 
0.7%
1300 23
 
0.6%
1450 22
 
0.6%
1000 22
 
0.6%
Other values (723) 1578
42.9%
(Missing) 1805
49.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

servant room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
2349 
1
1328 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2349
63.9%
1 1328
36.1%

Length

2024-02-12T22:16:28.959849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T22:16:29.033697image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2349
63.9%
1 1328
36.1%

Most occurring characters

ValueCountFrequency (%)
0 2349
63.9%
1 1328
36.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2349
63.9%
1 1328
36.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2349
63.9%
1 1328
36.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2349
63.9%
1 1328
36.1%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
3021 
1
656 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

Length

2024-02-12T22:16:29.110447image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T22:16:29.182829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

store room
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
3339 
1
338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

Length

2024-02-12T22:16:29.261085image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T22:16:29.332658image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
2972 
1
705 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2972
80.8%
1 705
 
19.2%

Length

2024-02-12T22:16:29.411158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T22:16:29.483416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2972
80.8%
1 705
 
19.2%

Most occurring characters

ValueCountFrequency (%)
0 2972
80.8%
1 705
 
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2972
80.8%
1 705
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2972
80.8%
1 705
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2972
80.8%
1 705
 
19.2%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
3272 
1
405 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

Length

2024-02-12T22:16:29.562089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T22:16:29.634445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

furniture_labels
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
2436 
2
1038 
1
 
203

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row2
4th row0
5th row2

Common Values

ValueCountFrequency (%)
0 2436
66.2%
2 1038
28.2%
1 203
 
5.5%

Length

2024-02-12T22:16:29.712915image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T22:16:29.790410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2436
66.2%
2 1038
28.2%
1 203
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 2436
66.2%
2 1038
28.2%
1 203
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2436
66.2%
2 1038
28.2%
1 203
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2436
66.2%
2 1038
28.2%
1 203
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2436
66.2%
2 1038
28.2%
1 203
 
5.5%

luxury_score
Real number (ℝ)

ZEROS 

Distinct116
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.621431
Minimum0
Maximum136
Zeros481
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-02-12T22:16:29.882228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q123
median45
Q384
95-th percentile136
Maximum136
Range136
Interquartile range (IQR)61

Descriptive statistics

Standard deviation41.706065
Coefficient of variation (CV)0.74982006
Kurtosis-0.87842812
Mean55.621431
Median Absolute Deviation (MAD)29
Skewness0.47593511
Sum204520
Variance1739.3958
MonotonicityNot monotonic
2024-02-12T22:16:29.993464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 481
 
13.1%
35 418
 
11.4%
136 215
 
5.8%
28 99
 
2.7%
21 83
 
2.3%
52 79
 
2.1%
15 73
 
2.0%
45 64
 
1.7%
30 63
 
1.7%
127 60
 
1.6%
Other values (106) 2042
55.5%
ValueCountFrequency (%)
0 481
13.1%
6 7
 
0.2%
7 52
 
1.4%
8 49
 
1.3%
9 1
 
< 0.1%
13 14
 
0.4%
14 34
 
0.9%
15 73
 
2.0%
16 26
 
0.7%
17 1
 
< 0.1%
ValueCountFrequency (%)
136 215
5.8%
130 20
 
0.5%
129 43
 
1.2%
128 17
 
0.5%
127 60
 
1.6%
123 10
 
0.3%
122 35
 
1.0%
121 24
 
0.7%
120 35
 
1.0%
119 18
 
0.5%

Interactions

2024-02-12T22:16:23.495682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:16.558869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:17.318805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:18.081636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:18.806715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:19.691957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:20.457527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:21.183784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:21.904937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:22.654887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:23.568508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:16.647027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:17.392727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:18.150451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:18.882627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:19.767415image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:20.528203image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:21.253772image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:21.982116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:22.727146image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:23.648240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:16.726189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:17.468571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:18.223427image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:19.074486image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:19.844370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:20.600648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:21.328331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:22.059962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:22.802992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:23.717869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:16.793867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:17.541360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:18.288887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:19.144657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:19.914523image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:20.673234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:21.397684image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:22.130524image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:22.877492image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:23.799039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:16.872238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:17.622560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:18.366337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:19.223968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:19.996510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:20.753034image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:21.478884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:22.212048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:22.954445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:23.879798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:16.952158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:17.702990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:18.441350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:19.304049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:20.075588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:20.831030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:21.553070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:22.293408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:23.133230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:23.951468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:17.022339image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:17.776230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:18.514396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:19.376520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:20.150571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:20.898999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:21.620898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:22.365913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:23.202494image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:24.024860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:17.092558image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:17.849889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:18.583241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:19.452314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:20.221583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:20.968907image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:21.691265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:22.430980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:23.275759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:24.105050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:17.171042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:17.929055image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:18.658979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:19.536336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:20.303796image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:21.040446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:21.756702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:22.508976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:23.343105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:24.181612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:17.242647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:18.002852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:18.732366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:19.612812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:20.378749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:21.108469image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:21.829249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:22.576123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-12T22:16:23.417008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-02-12T22:16:24.307234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-12T22:16:24.536850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

societyproperty_typesectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areaservant roompooja roomstore roomstudy roomothersfurniture_labelsluxury_score
0the lions cghsflatsector 561.9911055.01800.09Super Built up area 2400(222.97 sq.m.)Built Up area: 2000 sq.ft. (185.81 sq.m.)Carpet area: 1800 sq.ft. (167.23 sq.m.)443+NaNEastold property2400.02000.01800.001001292
1bestech park view residencyflatsector 20.986261.01565.25Super Built up area 1565(145.39 sq.m.)223NaNSouth-Westmoderately new1565.0NaNNaN00010075
2bptp freedom park lifeflatsector 575.508982.06123.36Built Up area: 5010 (465.44 sq.m.)563+NaNEastmoderately newNaN5010.0NaN110002120
3ss the leafflatsector 851.257179.01741.19Super Built up area 1741(161.74 sq.m.)Built Up area: 1730 sq.ft. (160.72 sq.m.)Carpet area: 1720 sq.ft. (159.79 sq.m.)223NaNSouth-Eastrelatively new1741.01730.01720.000000035
4vatika city homesflatsector 831.058571.01225.06Super Built up area 1740(161.65 sq.m.)Carpet area: 1225 sq.ft. (113.81 sq.m.)333NaNSouth-Eastmoderately new1740.0NaN1225.001000281
5aipl the peaceful homesflatsector 70a2.8011914.02350.18Super Built up area 2350(218.32 sq.m.)Carpet area: 1322 sq.ft. (122.82 sq.m.)34322.0North-Eastrelatively new2350.0NaN1322.010000035
6ss the leafflatsector 851.048320.01250.00Super Built up area 1640(152.36 sq.m.)Built Up area: 1550 sq.ft. (144 sq.m.)Carpet area: 1250 sq.ft. (116.13 sq.m.)223NaNEastnew property1640.01550.01250.0000010116
7international city by sobha phase 1housesector 1095.9024280.02429.98Plot area 270(225.75 sq.m.)4522.0Eastrelatively newNaN2430.0NaN10000290
8tulip violetflatsector 691.559822.01578.09Super Built up area 1578(146.6 sq.m.)332NaNNorth-Eastrelatively new1578.0NaNNaN010002127
9ats kocoonflatsector 1092.9510350.02850.24Built Up area: 3150 (292.64 sq.m.)Carpet area: 2850 sq.ft. (264.77 sq.m.)443+NaNSouth-Eastrelatively newNaN3150.02850.0101002120
societyproperty_typesectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areaservant roompooja roomstore roomstudy roomothersfurniture_labelsluxury_score
3792smart world orchardflatsector 611.6513750.01200.00Carpet area: 1200 (111.48 sq.m.)222NaNNaNunder constructionNaNNaN1200.000001008
3793smart world orchardflatsector 611.6013913.01150.00Carpet area: 1150 (106.84 sq.m.)222NaNNorth-Eastnew propertyNaNNaN1150.0000011020
3794mvn athensflatsohna road0.244210.0570.07Super Built up area 570(52.95 sq.m.)22112.0NaNrelatively new570.0NaNNaN00000030
3795sare crescent parcflatsector 921.004778.02092.93Built Up area: 2093 (194.45 sq.m.)443NaNNaNUndefinedNaN2093.0NaN0000000
3796vatika gurgaonflatsector 830.876987.01245.17Super Built up area 1245(115.66 sq.m.)222NaNEastmoderately new1245.0NaNNaN00000021
3798emaar gurgaon greensflatsector 1021.4013690.01022.64Super Built up area 1650(153.29 sq.m.)Carpet area: 1022.58 sq.ft. (95 sq.m.)333NaNEastrelatively new1650.0NaN1022.5810000052
3799dlf the arbourflatsector 638.5021519.03950.00Built Up area: 3950 (366.97 sq.m.)44NoNaNNaNUndefinedNaN3950.0NaN00000054
3800emaar mgf emerald floors premierflatsector 652.8016969.01650.07Super Built up area 1975(183.48 sq.m.)Built Up area: 1800 sq.ft. (167.23 sq.m.)Carpet area: 1650 sq.ft. (153.29 sq.m.)4432.0Northrelatively new1975.01800.01650.0000010267
3801signature global syneraflatsector 810.488450.0568.05Super Built up area 657(61.04 sq.m.)Carpet area: 568 sq.ft. (52.77 sq.m.)221NaNSouth-Eastrelatively new657.0NaN568.0000000263
3802independenthousesector 4111.0033951.03239.96Plot area 360(301.01 sq.m.)4423.0Eastnew propertyNaN3240.0NaN11010021